A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region.

This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification.

Bibliographic Details

Main Authors:	LI, G., LU, D., DUTRA, L., BATISTELLA, M.
Other Authors:	GUIYING LI, INDIANA UNIVERSITY; DENGSHENG LU, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM.
Format:	Artigo de periódico biblioteca
Language:	English eng
Published:	2012-05-17
Subjects:	ALOS PALSAR, RADARSAT, Land-cover classification, Amazon., texture.,
Online Access:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/924819
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